What is Open Knowledge Graph?

 Based on the GitHub repository you linked, **Open Knowledge Graphs** (OKG) is a **static, daily-refreshed catalog of ontologies and semantic software tools**. It acts as a comprehensive, machine-readable directory sourced entirely from Wikidata.

### 🎯 Core Purpose

OKG solves the problem of discoverability in the semantic web space. Instead of searching across scattered sources, it provides a centralized, structured catalog where you can find:

*   **Ontologies, Vocabularies, and Taxonomies:** Formal knowledge structures used to model data.

*   **Semantic Software:** Tools like RDF triplestores, reasoners, and graph databases.

### ⚙️ How It Works: Data Pipeline & Access

1.  **Data Source:** The project runs a Python script (`fetch_data.py`) that queries the Wikidata Query Service (WDQS) daily. It pulls records for entities that are instances of "ontology" or "semantic software."

2.  **Enrichment:** It optionally uses an LLM (via Anthropic API) to assign domain categories (e.g., Life Sciences, Finance) to records, which are then frozen in a local file (`categories.json`) to ensure stability.

3.  **Publication:** The results are published as both **machine-readable datasets** (in Turtle RDF and JSON formats) and a **human-friendly searchable website**.

### 📊 Key Resources & APIs

The project offers several ways to consume the data:

| Access Method | Description | Example Link/Use |

| :--- | :--- | :--- |

| **Web UI** | A searchable browser interface for human users. | [openknowledgegraphs.com](https://openknowledgegraphs.com/) |

| **RDF Datasets** | Machine-readable semantic web data in Turtle format. | `ontologies.ttl`, `software.ttl` |

| **JSON Datasets** | Structured JSON files for easy parsing in applications. | `ontologies.json`, `software.json` |

| **Search API** | A semantic search API over the full catalog. | `GET https://api.openknowledgegraphs.com/search?q=movie+ontology` |

| **MCP Server** | A Model Context Protocol server that exposes OKG tools to AI assistants like Claude Desktop or Cursor. | Tools include `okg_search`, `okg_search_ontologies` |

### 💡 Why It's Useful

*   **For AI/LLM Development:** You can use OKG to discover authoritative ontologies to ground your RAG system, or to find software tools to manage your knowledge graph. The MCP server makes this directly accessible to AI agents.

*   **For Ontology Engineers:** It provides a curated starting point to find existing ontologies in a domain rather than reinventing the wheel.

*   **For Transparency:** Since it's a static site generated from open data, the entire catalog is transparent, forkable, and auditable.

In short, OKG is like a "Yellow Pages" for the semantic web, helping you find the right ontology or tool for your knowledge engineering project.

If you'd like to understand how to use the MCP server with an AI assistant, or how the data models are structured, feel free to ask